-
and particles, while the latter can induce radial redistribution or losses of EPs, limiting their contribution to plasma heating. Traditionally, these processes have been studied separately, assuming
-
in the training data and can naturally arrive during deployment (i.e., a distribution shift), increasing the risk of obtaining wrong predictions. Consequently, OoD samples detection is crucial in
-
. Moyens / Méthodes / Logiciels Matlab, Python, Signal processing, Digital communications Profil du candidat Required skills: •Signal processing and digital communication •MATLAB/Python •Proficient in using
-
of quantitative methods of data processing and analysis, optimization and scientific programming, in the following areas. * The search, structuration and analysis of economic, energy and emissions-related data